The systematic errors caused by temporal spatial variation of sound speed and sonar signal delays significantly reduce the positioning accuracy of deep seafloor datum points. To overcome this problem, a Kalman filter (KF) based on two-step systematic error estimation is proposed for the acoustic positioning of deep seafloor datum points. The proposed algorithm first calculates the equivalent sound speed by sound ray tracing. A KF based on first-step systematic error estimation is then constructed to calculate the parameters of position and first-step systematic error, and to obtain the slant range residuals. Based on the slant range residuals, the systematic error related to long-period errors of sound speed is parameterized by empirical mode decomposition (EMD) and function fitting. Finally, a KF based on second-step systematic error estimation is used to calculate the parameters of position and second-step systematic error. The proposed algorithm is verified by a real experiment for the acoustic positioning of deep seafloor datum points. The results demonstrate that the proposed algorithm significantly improves the three-dimensional positioning accuracy of deep seafloor datum points compared with the least squares (LS) traditional underwater positioning model, which does not consider the influence of systematic errors, and a KF based on the single systematic error estimation. Using the proposed method, the root mean square (RMS) of the slant range residuals for deep seafloor datum points can be better than 13 cm.

声速时空变化和声纳信号延迟引起的系统误差显着降低了深海底基准点的定位精度。为了克服这个问题，提出了一种基于两步系统误差估计的卡尔曼滤波器（KF）用于深海底基准点的声学定位。该算法首先通过声射线追踪计算等效声速。然后构造基于第一步系统误差估计的KF，计算位置参数和第一步系统误差参数，得到斜距残差。基于斜距残差，与声速长周期误差相关的系统误差通过经验模态分解（EMD）和函数拟合进行参数化。最后，使用基于第二步系统误差估计的KF计算位置参数和第二步系统误差参数。提出的算法通过真实的海底基准点声学定位实验得到验证。结果表明，与不考虑系统误差影响的最小二乘法(LS)传统水下定位模型和基于单次定位的KF相比，该算法显着提高了深海海底基准点的三维定位精度。系统误差估计。使用所提出的方法，深海底基准点的斜距残差的均方根 (RMS) 可以优于 13 cm。提出的算法通过真实的海底基准点声学定位实验得到验证。结果表明，与不考虑系统误差影响的最小二乘法(LS)传统水下定位模型和基于单次定位的KF相比，该算法显着提高了深海海底基准点的三维定位精度。系统误差估计。使用所提出的方法，深海底基准点的斜距残差的均方根 (RMS) 可以优于 13 cm。提出的算法通过真实的海底基准点声学定位实验得到验证。结果表明，与不考虑系统误差影响的最小二乘法(LS)传统水下定位模型和基于单次定位的KF相比，该算法显着提高了深海海底基准点的三维定位精度。系统误差估计。使用所提出的方法，深海底基准点的斜距残差的均方根 (RMS) 可以优于 13 cm。结果表明，与不考虑系统误差影响的最小二乘法(LS)传统水下定位模型和基于单次定位的KF相比，该算法显着提高了深海海底基准点的三维定位精度。系统误差估计。使用所提出的方法，深海底基准点的斜距残差的均方根 (RMS) 可以优于 13 cm。结果表明，与不考虑系统误差影响的最小二乘法(LS)传统水下定位模型和基于单次定位的KF相比，该算法显着提高了深海海底基准点的三维定位精度。系统误差估计。使用所提出的方法，深海底基准点的斜距残差的均方根 (RMS) 可以优于 13 cm。